Publications

@inproceedings{gaguilar2018,

title = {Modeling Noisiness to Recognize Named Entities using Multitask Neural Networks on Social Media},

author = {Gustavo Aguilar and A. Pastor López Monroy and Fabio A. Gonzalez and Thamar Solorio},

editor = {Association for Computational Linguistics },

url = {http://www.aclweb.org/anthology/N18-1127},

year  = {2018},

date = {2018-06-01},

booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},

publisher = {Association for Computational Linguistics},

address = {New Orleans, Louisiana},

abstract = {Recognizing named entities in a document is a key task in many NLP applications. Although current state-of-the-art approaches to this task reach a high performance on clean text (e.g. newswire genres), those algorithms dramatically degrade when they are moved to noisy environments such as social media domains. We present two systems that address the challenges of processing social media data using character-level phonetics and phonology, word embeddings, and Part-of-Speech tags as features. The first model is a multitask end-to-end Bidirectional Long Short-Term Memory (BLSTM)-Conditional Random Field (CRF) network whose output layer contains two CRF classifiers. The second model uses a multitask BLSTM network as feature extractor that transfers the learning to a CRF classifier for the final prediction. Our systems outperform the current F1 scores from state-of-the-art on the Workshop on Noisy User-generated Text 2017 dataset by 2.45% and 3.69%, establishing a more suitable approach for social media environments. },

keywords = {CRF, Multitask, NER, Phonetics, Phonology, Social Media},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{aguilar-EtAl:2017:WNUT,

title = {A Multi-task Approach for Named Entity Recognition on Social Media Data},

author = {Gustavo Aguilar, Suraj Maharjan, A. Pastor López Monroy, Thamar Solorio},

url = {http://www.aclweb.org/anthology/W17-4419},

year  = {2017},

date = {2017-09-07},

publisher = {Proceedings of 3rd Workshop on Noisy User-generated Text, WNUT 2017.},

abstract = {Named Entity Recognition for social media data is challenging because of its inherent noisiness. In addition to improper grammatical structures, it contains spelling inconsistencies and numerous informal abbreviations. We propose a novel multi-task approach by employing a more general secondary task of Named Entity (NE) segmentation together with the primary task of fine-grained NE categorization. The multi-task neural network architecture learns higher order feature representations from word and character sequences along with basic Part-of-Speech tags and gazetteer information. This neural network acts as a feature extractor to feed a Conditional Random Fields classifier. We were able to obtain the first position in the 3rd Workshop on Noisy User-generated Text (WNUT-2017) with a 41.86% entity F1-score and a 40.24% surface F1-score.},

note = {Ranked 1st place in the two evaluation metrics},

keywords = {CRF, Deeplearning, Multitask, NER},

pubstate = {published},

tppubtype = {inproceedings}

}